https://github.com/cmendl/hubbard-dqmc

Determinant quantum Monte Carlo applied to the Hubbard model

https://github.com/cmendl/hubbard-dqmc

Repository

Determinant quantum Monte Carlo applied to the Hubbard model

https://github.com/cmendl/hubbard-dqmc/blob/master/

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Determinant quantum Monte Carlo implementation
==============================================

C implementation of the determinant quantum Monte Carlo (DQMC) method applied to Hubbard-type models.


Quickstart
----------
The code depends on the [CBLAS](http://www.netlib.org/blas/#_cblas) and [LAPACKE](http://netlib.org/lapack/lapacke.html) libraries. These can be installed via `sudo apt install libblas-dev liblapacke-dev` (on Ubuntu Linux) or similar. Alternatively, the *Makefile* shows how to use the Intel compiler with MKL.

Call `make` to build the project. You might have to adapt some parameters in the *Makefile* beforehand (see the comments there).

To run the code, cd into the *bin* subfolder and call `hubbard_dqmc `; some example parameter files are provided there. For unit testing, cd into the *test* subfolder and execute `run_tests`.

The Mathematica unit test notebooks can be opened by [Mathematica](https://www.wolfram.com/mathematica) or the free [CDF player](https://www.wolfram.com/cdf-player).


About
-----
Copyright (c) 2015-2017, Edwin Huang and Christian B. Mendl

This code was developed at Stanford University with support from the U.S. Department of Energy (DOE), Office of Basic Energy Sciences, Division of Materials Sciences and Engineering, under Contract No. DE-AC02-76SF00515.


References
----------
1. R. Blankenbecler, D. J. Scalapino, R. L. Sugar  
   Monte Carlo calculations of coupled boson-fermion systems. I  
   Phys. Rev. D 24, 2278 (1981) [DOI](https://doi.org/10.1103/PhysRevD.24.2278)
2. S. R. White, D. J. Scalapino, R. L. Sugar, E. Y. Loh, J. E. Gubernatis, and R. T. Scalettar  
   Numerical study of the two-dimensional Hubbard model  
   Phys. Rev. B 40, 506-516 (1989) [DOI](https://doi.org/10.1103/PhysRevB.40.506)
3. Z. Bai, C.-R. Lee, R.-C. Li, S. Xu  
   Stable solutions of linear systems involving long chain of matrix multiplications  
   Linear Algebra Appl. 435, 659-673 (2011) [DOI](https://doi.org/10.1016/j.laa.2010.06.023)
4. A. Tomas, C.-C. Chang, R. Scalettar, Z. Bai  
   Advancing large scale many-body QMC simulations on GPU accelerated multicore systems  
   IEEE 26th International Parallel & Distributed Processing Symposium (IPDPS) 308-319 (2012) [DOI](https://doi.org/10.1109/IPDPS.2012.37)
5. S. Gogolenko, Z. Bai, R. Scalettar  
   Structured orthogonal inversion of block p-cyclic matrices on multicore with GPU accelerators  
   Euro-Par 2014 Parallel Processing, LNCS 8632, pages 524-535 (2014) [DOI](https://doi.org/10.1007/978-3-319-09873-9_44)
6. C. Jiang, Z. Bai, R. Scalettar  
   A fast selected inversion algorithm for Green's function calculation in many-body quantum Monte Carlo simulations  
   IEEE International Parallel and Distributed Processing Symposium, 2016 [DOI](https://doi.org/10.1109/IPDPS.2016.69)

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